Automated computational analysis of nanoparticles is the key approach urgently required to achieve further progress in catalysis, the development of new nanoscale materials, and applications. Analysis of nanoscale objects on the surface relies heavily on scanning electron microscopy (SEM) as the experimental analytic method, allowing direct observation of nanoscale structures and morphology. One of the important examples of such objects is palladium on carbon catalysts, allowing access to various chemical reactions in laboratories and industry. SEM images of Pd/C catalysts show a large number of nanoparticles that are usually analyzed manually. Manual analysis of a statistically significant number of nanoparticles is a tedious and highly time-consuming task that is impossible to perform in a reasonable amount of time for practically needed large amounts of samples. This work provides a comprehensive comparison of various computer vision methods for the detection of metal nanoparticles. In addition, multiple new types of data representations were developed, and their applicability in practice was assessed.

Abstract. In our previous work, an exponential approximation (EA) method was proposed for the detection of nanoparticles in scanning electron microscope (SEM) images. It shows the best quality of nanoparticle detection compared to other methods. But its main drawback is that it takes a lot of time. In this paper, we propose a two-level parallel computing scheme and a corresponding high-performance python+MPI implementation of the EA method. Experiments have shown that the developed parallel implementation can significantly speed up the computational process.